PurpleAir
The R client for PurpleAir sensors keeps finding its time-averaging was wrong.
Comparison · Analytics
assesslite vs glmbayes
A side-by-side editorial comparison of assesslite and glmbayes — release velocity, themes, recent moves, and the top alternatives to consider.
assesslite vs glmbayes: at a glance
What is assesslite?
Four releases in fifteen hours take causal assumption-checking from resampling to identification
AssessLite attacks the structural assumptions behind a causal finding and returns three-way verdicts — stable, unstable, or not resolvable — feeding proceed, conditional or abstain decisions, with an auditable JSON record validated against a shared schema. It runs natively in R and Python against one spec, with the Python engine reproducing R's coxph(ties=breslow) exactly. The entire 0.1.0-through-0.4.0 arc landed inside a single day in July 2026.
What is glmbayes?
A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain
glmbayes fits Bayesian generalized linear models with optional OpenCL acceleration. The last four months moved it from a package with its own vocabulary to one that answers the insight and bayestestR generics the rest of the R Bayesian ecosystem is built on, while pushing the OpenCL kernels out into a separate nmathopencl dependency that carries CRAN Windows binaries. It returned to CRAN in August after an archival over a configure policy issue.
assesslite vs glmbayes: editorial side-by-side
assesslite
0.0ANALYTICS
Four releases in fifteen hours take causal assumption-checking from resampling to identification
◆ Current state
AssessLite attacks the structural assumptions behind a causal finding and returns three-way verdicts — stable, unstable, or not resolvable — feeding proceed, conditional or abstain decisions, with an auditable JSON record validated against a shared schema. It runs natively in R and Python against one spec, with the Python engine reproducing R's coxph(ties=breslow) exactly. The entire 0.1.0-through-0.4.0 arc landed inside a single day in July 2026.
◆ Where it's heading
The releases are cumulative, each restating the previous feature set and adding to it, so read them as one launch rather than four. The direction across that launch is clear: it started with resampling attacks (permutation, holdout, temporal split, subgroup), turned toward causal identification with declared DAGs and the backdoor criterion, then reached into genuinely dependent data with spatial and interference checks. The correctness work moves in step — the 0.3.0 Bonferroni adjustment fixed a holdout rule that was flagging roughly m times too often with m variants.
◆ Prediction
The project has repeatedly shipped what it previously listed as future work within days, so the next release most likely converts another declared gap rather than opening a new front.
glmbayes
6.3ANALYTICS
A GPU-accelerated Bayesian GLM package buys its way into the standard R Bayesian toolchain
◆ Current state
glmbayes fits Bayesian generalized linear models with optional OpenCL acceleration. The last four months moved it from a package with its own vocabulary to one that answers the insight and bayestestR generics the rest of the R Bayesian ecosystem is built on, while pushing the OpenCL kernels out into a separate nmathopencl dependency that carries CRAN Windows binaries. It returned to CRAN in August after an archival over a configure policy issue.
◆ Where it's heading
The arc is about removing reasons not to use it. GPU support was previously blocked on Windows because the OpenCL kernels were vendored; splitting them into a CRAN package with binaries fixed that. The ecosystem work does the same thing for tooling — a glmb fit now responds to get_parameters, get_priors, simulate_prior and check_prior, so it drops into workflows built around easystats rather than requiring its own. The CRAN archival and the configure fixes that followed show how much of the effort goes into distribution rather than modelling.
◆ Prediction
get_priors() returning the full prior specification rather than a marginal table is the kind of detail that invites further bayestestR integration, and the diagnostic surface is the least built-out part of what has shipped so far.
Alternatives to assesslite and glmbayes
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either assesslite or glmbayes.
piecepackr
A board game graphics package runs one of the most disciplined deprecation cycles in R.
e2tree
The explainable-ensemble-tree package now measures whether its own explanations are faithful.
discretefdr
The discrete-data FDR package is being pared into one piece of a larger multiple-testing suite.
contentanalysis
A scientific-text analysis package moved from counting citations to classifying argument structure.
relialearnr
The teaching arm of an R reliability suite keeps pace with whatever its analysis siblings ship.
See all assesslite alternatives → · See all glmbayes alternatives →
Recent activity from assesslite and glmbayes
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 10d agoglmbayesBack on CRAN after a configure policy fix
- 22d agoglmbayesOpenCL split out to nmathopencl; insight and bayestestR integration ⚡
- 1mo agoassessliteAssessLite 0.4.0
- 1mo agoassessliteAssessLite 0.3.0
- 1mo agoassessliteAssessLite 0.2.0 ⚡
- 1mo agoassessliteAssessLite 0.1.0 ⚡
- 1mo agoglmbayesMulti-response models and conjugate GLM priors
- 3mo agoglmbayesOpenCL kernels restructured and a binomial GPU bug fixed
- 3mo agoglmbayesVersion bump for CRAN resubmission
- 1y agoglmbayesCRAN-ready beta with the core S3 interface
Frequently asked questions
What is the difference between assesslite and glmbayes?
They serve adjacent needs but don't currently overlap on shipped themes. glmbayes is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is assesslite better than glmbayes?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. glmbayes is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to assesslite?
Top assesslite alternatives in Analytics are ranked by recent ship velocity. Browse the "assesslite alternatives" section above for the current picks, or visit /alternatives/assesslite for the full list with editorial commentary on each.
What are the best alternatives to glmbayes?
Top glmbayes alternatives in Analytics are ranked by recent ship velocity. Browse the "glmbayes alternatives" section above for the current picks, or visit /alternatives/glmbayes for the full list with editorial commentary on each.